Generative object compositing emerges as a promising new avenue for
compositional image editing. However, the requirement of object identity
preservation poses a significant challenge, limiting practical usage of most
existing methods. In response, this paper introduces IMPRINT, a novel
diffusion-based generative model trained with a two-stage learning framework
that decouples learning of identity preservation from that of compositing. The
first stage is targeted for context-agnostic, identity-preserving pretraining
of the object encoder, enabling the encoder to learn an embedding that is both
view-invariant and conducive to enhanced detail preservation. The subsequent
stage leverages this representation to learn seamless harmonization of the
object composited to the background. In addition, IMPRINT incorporates a
shape-guidance mechanism offering user-directed control over the compositing
process. Extensive experiments demonstrate that IMPRINT significantly
outperforms existing methods and various baselines on identity preservation and
composition quality.

本文介绍了一种名为 IMPRINT 的新型扩散式生成模型，使用两阶段学习框架，将保持对象身份的学习与组合学习分离开来，实现了上下文无关的身份保留预训练和背景下对象无缝融合的学习，同时引入了一种形状引导机制，提供用户对组合过程的控制。大量实验证明，IMPRINT 在身份保留和组合质量方面明显优于现有方法和各种基线方法。